TN has a few characteristics that differentiate it from other natural language processing (NLP) tasks. First, the normalized texts are scarce and difficult to collect [1]}, [2]}. To our knowledge, Google text normalization dataset [3]} is the only sizeable public dataset. Second, TN applications such as mobile assistants demand very low error rates [4]}. Errors are called unrecoverable if they alter the input semantic [4]}, e.g. \( \textit { ``1/4"} \rightarrow \ \textit { ``one fourteenth"}\) . Information-sensitive industry sectors such as finance can not tolerate any chance of unrecoverable errors. Lastly, semiotic tokens make up only a small percentage of the sentence, but they stem from a huge variety of semiotic classes [4]}. These make TN a challenging NLP task, and it remains an active area of research.
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